We regressed compliance on social conformity, approval of the rules, and trust in science. There was a significant effect of conformity (\(b=0.4\), \(t=34\), \(p<.01\)) and of approval (\(b=0.27\), \(t=22\), \(p<.01\)) but not of trust in science (\(b=0\), \(t= 0.8\), \(p=.48\)).

(source: https://www.autodesk.com/research/publications/same-stats-different-graphs)

(source: https://www.autodesk.com/research/publications/same-stats-different-graphs)

• Be able to built up a plot in layers

• Flexible in how you map data to graphical elements

• Can be tricky at the beginning, but gets easier

• Remember: rely on googling error messages + trying stuff

• Data \(\longrightarrow\) columns
• Plot \(\longrightarrow\) elements:
  • x, y axes
  • color, fill
  • size, shape, linetype, …
• Aesthetic aes() is just mapping data column to plot element

• How to picture each aesthetic
  • Bar
  • Point
  • Line
  • Density curve/violin/etc.

data <- data.frame(score=rnorm(80, 5, 4))
myplot <- ggplot(data, aes(x=score))

myplot + geom_density()

myplot + geom_histogram()

myplot + geom_dotplot()

ggplot(data_scatter,
       aes(x=x,
           y=outcome)) + 
  geom_point()

ggplot(data_scatter,
       aes(x=x,
           y=outcome)) + 
  geom_point(alpha=0.4,
             color="royalblue")

ggplot(weight_data,
       aes(x=height_cm,
           y=weight_kg,
           color=gender)) + 
  geom_point(alpha=0.4)

ggplot(data_time, 
       aes(x=time,
           y=value,
           color=stock)) + 
  geom_line()

stat_summary(fun=…) - using the same data, generate some summaries

stat_smooth(method=…) - generate a spline

ggplot(weight_data, 
       aes(x=gender, y=weight_kg)) + 
  geom_point(alpha=0.4) + 
  stat_summary(fun = "mean",
               color = "red",
               geom = "point",
               shape = 18,
               size = 4)

If you’re ever having trouble with stat_summary geom=“line” (lines look crazy, lots of up/down lines): add aes(group=…)

ggplot(full_weight_data,
       aes(x=height_cm,y=weight_kg)) + 
  geom_point(alpha=0.4)+
  stat_smooth()

ggplot(full_weight_data,
       aes(x=height_cm,y=weight_kg)) + 
  geom_point(alpha=0.4)+
  stat_smooth(method=lm)

It’s very tempting to just use the default column names

But it is definitely worth adding more explicit labels:

• labs(x=…, y=…, color=…, title=…)

• There are heaps of nice extra packages
  • RColorBrewer
  • ggcorrplot
  • see https://www.r-graph-gallery.com

• Using weight_data, create a plot of weight ~ gender

• Use all of the following geoms/stats:

  • geom_violin
  • geom_point with position=position_jitter()
  • stat_summary (for the mean)

• Make nice labels

• IF you have enough time:

  • use geom_errorbar() to add confidence intervals
    • hint: you can have different datasets for different geoms
    • use the bootMeans function I’ve provided below
  • add geom_segment and geom_annotate for significance bars/stars